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Title: Managerial rules for recovering from a disruption event in liner shipping
Author: Lekhavat, Saowanit
ISNI:       0000 0004 7660 7490
Awarding Body: Brunel University London
Current Institution: Brunel University
Date of Award: 2019
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The aim of this study is to propose managerial rules for recovering from a disruption event in liner shipping. A critical realism philosophy is adopted in the design of the research. Optimisation and an experimental methodology which follows the critical realism paradigm is used as a framework. Particle swarm optimisation (PSO) is an optimisation model in which various rules are implemented to search for the optimal option to recover from a disruption problem. Solution representations for two options, speeding up and skipping, have been designed. A case study of a trans- Pacific route is used to generate novelty in the model under various configurations of degrees of disruption, maximum speeds, fuel prices, time windows and skipping penalties. The results show that the skipping option performs better than the speeding up option when there is a large amount of delay. The port skipping option is more valuable when the maximum speed limit of a vessel is low. The option of port skipping saves more total cost than the option of speeding up when fuel prices increase. Particularly, a vessel which applies the skipping option can save more total cost than one which applies the speeding option when there are high fuel prices and high degrees of disruption. In other words, speeding up is recommended in the case of low fuel prices and low degrees of disruption. The speeding option is recommended when a vessel faces a short delay and has a long time window. In contrast, the skipping option is more valuable when there is a long delay and a short time window. The higher the skipping delay penalties, the more valuable the speeding option is.
Supervisor: Lee, H. ; Mansouri, S. A. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Optimisation ; Maritime ; Simulation ; Transportation